基于EMD和球结构SVM的滚动轴承故障诊断  

Roller Bearing Fault Diagnosis by Using Empirical Mode Decomposition and Sphere-Structured Support Vector Machine

作　　者：杨洁明[1] 田英[1] 

机构地区：[1]太原理工大学机械电子工程研究所,太原030024

出　　处：《振动．测试与诊断》2009年第2期155-158,共4页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(编号:50335030);山西省自然科学基金资助项目(编号:2006011056)

摘　　要：提出了一种基于经验模式分解(Empirical Mode Decomposition,简称EMD)和球结构支持向量机的滚动轴承故障诊断方法。该方法以EMD的能量特征向量作为输入来建立球结构支持向量机分类器,识别滚动轴承的故障类型,通过麦克斯韦的三角平面坐标色度图方法进行可视化验证,并且与小波包能量特征向量作为输入的球结构支持向量机诊断方法进行比较。结果表明,用EMD能量法作预处理更能准确地提取故障特征量,有更高的故障识别率。A roller bearing fault diagnosis method based on empirical mode decomposition(EMD) and sphere-structured support vector machine(SVM) is put forward. The method took the EMD energy feature vectors as the input parameters to establish the sphere-structured SVM identifying the roller bearing fault pattern,every clustering was visualized for verifying it by the chromaticity diagram of Maxwell's triangle plane coordinate. Compared with the method of wavelet packet energy feature vector establishing the sphere-structured SVM,the method of EMD energy for pre-processing can extract fault feature vector more exactly and identify fault pattern accurately and effectively.

关 键 词：故障诊断 经验模式分解 滚动轴承 球结构支持向量机 小波包 

分 类 号：TH133[机械工程—机械制造及自动化] TP206.3[自动化与计算机技术—检测技术与自动化装置]